deviantArt + Artworks

Updated: Aug 3, 2018

deviantArt (dA) is an online community dedicated to sharing user-generated artworks. Launched in 2000, today this initiative has 44 million members from over 190 countries. deviantArt has stopped updating the number of artworks it hosts since a few years now. Compared to sites such as Flickr, Instagram or Pinterest, where the majority of user generated content comes from photographs, dA's archive might seem small. Still, the stylistic space it occupies, the different genres that are uploaded are rather unique, considering the majority of these works do not belong to professional artists.

Visualizations of 90.000 image in top category Digital Art (left), and 90,000 images in top category Traditional Art (right). X-axis: average brightness. Y-axis: average saturation.

We started our work with a network analysis of the category structure of dA, before doing any image analysis. The results of our analysis showed that artists who work in the genre of 'traditional arts' do most of the time also use digital tools to generate 'digital arts'. Hence, to analyze these two prominent categories, a new methodology to study both the content and the structure of the social network is developed where social network analysis in combination with cultural analytics is applied. The results showed that even though a high proportion of artists produce in both Digital Art and Traditional Art categories, the resulting artworks show stylistic differences that arise due to the differences in the production technique.

For our next work in image analysis, we rather used dA image archive as representative of 21st century art, and collected artworks from the 'abstract art' category. Comparing these artworks with the collection of MART Museum of Modern Art Trento museums' abstract artworks, we tested if we can automatically define the emotional impact of artworks. The classification of images based on the emotions they evoke is a recent approach in multimedia retrieval community. We employed a state-of-the-art recognition system to understand which statistical patterns are associated with positive and negative emotions on two different datasets that comprise professional and amateur abstract artworks.

Another work related closely with this research was the analysis of abstract artworks with alternatives titles, to see the impact of naming on the abstract artworks. Several psychological works observed that the metadata (i.e., titles, description and/or artist statements) associated with paintings increase the understanding and the aesthetic appreciation of artworks. We applied sentiment analysis on painting titles, and investigated how the texture of an artwork and the accompanying title affect the emotions evoked in the audience.

We continue our research in image analysis, and our latest question is if we can detect style transfer between artworks. dA offers the ideal archive for this question, as stock images are shared exactly for this purpose. The use of visual elements of an existing image while creating new ones is a commonly observed phenomenon in digital artworks. The practice, which is referred to as image reuse, is not an easy one to detect even with the human eye, less so using computational methods. The automatic image reuse detection in digital artworks was treated as an image retrieval problem. A set of existing image descriptors for image reuse detection were evaluated, providing a baseline for the detection of image reuse in digital artworks. An image retrieval method tailored for reuse detection was introduced, by combining saliency maps with image descriptors.